chapter1 color image processing
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Chapter 1
Introduction: Colour Image Processing
1.1 Introduction
Colour is a perceived phenomenon, not a physical property of light [San98]. Among all the
human senses, sight and the perception of colours should be perhaps the most fascinating. Most
of image processing has been associated with binary or black & white (B&W) images. This is
largely because of the high cost and limited availability of sensors and processing resources in
the past. Colour image usually requires about 64K times more data (considering that 8 bits are
needed for B&W and 24 for colour) to process. Colour has really been studied for the last 25
years and only recently low cost sensors and computing power are readily available. With the
appearance of computers with larger amounts of memory and faster speeds, the processing of
colour images is now more plausible to realise.
With cheaper sensors a need to explore different low cost processing is needed. Hence, special
purpose hardware, which gives real-time performance, is a very attractive alternative. This
thesis explores an alternative of the realisation and implementation of colour image processing
in dedicated hardware as opposed to that of using a general-purpose machine, i.e. personal
computer. These realisations would translate into much faster speeds permitting real-time
processing, which are important to various sectors such as industry, medicine, etc.. Also, by
having a dedicated hardware, costs are dramatically reduced.
In this work, a Universal Colour Transformation Hardware (UCTH) system that operates at
real-time video rate is proposed. The UCTH is capable of performing two objectives. First,
represent colour in a convenient way and second, implement limited colour image processing
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algorithms. Since most colour image processing applications rely on an appropriate colour
space to carry out the algorithms and several references exist in the literature, the UCTH
permits transformations for Red Green Blue (RGB) to alternative colour spaces. These can beset-up by the user for a specific application and then realise some particular digital image-
processing task, e.g. colour segmentation, hue shifting, clustering, edge detection, etc.
In order to be able to handle and manipulate colour acquired from capturing devices such as
single-chip or three-chip cameras, it is essential to understand the mechanisms of colour vision,
the representation of colour and the capabilities and restrictions of colour imaging devices. The
next sections of this chapter explore these issues and at the end, the organisation of this thesis is
presented.
1.2 The history of colour
Although colour has always existed, it was Sir Isaac Newtons experiments in Trinity College,
Cambridge in 1666 that established a physical basis for colour [Fau88] [Coh95]. With the use
of sunlight and a prism, Newton discovered that white light could be made to separate into aseries of different colours. Also, he determined that the effective refractive power of the glass
varied according to the position of the colour in this sequence, with red deviating least in angle
(i.e. longest wavelength) and blue the most (i.e. shortest wavelength). The term spectrum was
created by Newton to describe the ghostly effect quality of this effect.
A better understanding of light and colour occurred when Thomas Young in 1801 hypothesised
that human eyes have three receptors and the difference in their responses contributes to the
sensation of colour [Mac93]. He demonstrated that by overlapping three primaries lights having
the principal colours of Red, Green and Blue-Violet he could obtain the secondary colours
Yellow, Cyan and Magenta [Jac94].
Another way of producing additive colour is by using a spinning disc with coloured regions
having adjustable sectors and a central coloured area. During the 1850s James Clerk Maxwell
[Max95], with his discs, found that by adjusting the sectors and spinning the disc fast enough,
the sectors appear to fuse matching the colour in the central area, a phenomenon now referred
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to as trichromatic generalization or trichromacy [Sha97]. Around that time, Helmholtz
[Mac70] explained the distinction between additive and subtractive colour mixing and
explained trichromacy in terms of spectral sensitivity curves of the three colour sensing fibresin the eye. Trichromacy indicated the fact that the human eye has three colour receptors known
as the S, M and L cones for short, medium and long wave sensitivities, respectively, that can
now be determined through psychophysical experiments [Sto93].
Before these measurements were possible, Colour Matching Functions (CMFs) were
determined through psychophysical experiments [Wys00]. Here a normal observer matched any
spectral light to mixture of three fixed-colour primary lights. The observer sees a split
(bipartite) fields where a test colour has to be matched by varying the contribution of the 3
primary lights that generate the mixture. This forms the basis of all colorimetry.
1.3 Colorimetry
The science of colour and its measurement is known as colorimetry. The Commission
Internationale de lEclairage (CIE) is the main organisation responsible of colour metrics andterminology. The first colour specification was developed by the CIE in 1931 and continues to
form the basis of modern colorimetry [CIE86]. The following terms have been defined by the
CIE and are given in [Hun91]:
Brightness: The human sensation by which an area exhibits more or less light.
Hue: The human sensation according to which an area appears to be similar to one, or
two proportions of two of the perceived colours red, yellow, green and blue.
Lightness: The sensation of an areas brightness relative to a reference white in a scene.
Chroma: The colourfulness of an area relative to the brightness of a reference white.
Saturation: The colourfulness of an area relative to its brightness.
A colour, therefore, is a visual sensation produced by a specific spectral power distribution
(SPD) incident on the retina.
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The CIE system works by weighting SPD of an object in terms of the human vision system
(HSV) by providing two different but equivalent sets of CMFs. The first set, know as the
CIERGB (Red-Green-Blue) associated with monochromatic primaries at wavelengths of 700,546.1 and 435.1 nm, respectively [Clu72] and is depicted on the left of Figure 1.1. Their radiant
intensities are adjusted so that the tristimulus values have a constant spectral radiance. The
second set of CMFs, known as the CIEXYZ are shown in right portion of Figure 1.1. A set of
three artificial primariesX, YandZwhere created [Fai98] for CIEXYZ to avoid negative values
appearing in the CIERGB which simplifies operations. CIEXYZ is defined in terms of a linear
transformation of CIERGB and since an infinite number of transformations can be defined in
order to meet this non-negativity requirement this is not a physically realisable space [Sha97].
Figure 1.1: Spectral tristimulus values of the CIE 1931 standard colorimetric observer for
CIERGB (left) and CIEXYZ (right).
1.4 Colour models
Other common terms for colour models include colourspaces, colourcoordinatesystems and
are three-dimensional (3-D) arrangements of colour sensations where colour are specified by
points in these spaces [San98]. The colour models are used to classify colours and to qualify
them according to such attributes as hue, saturation, chroma, lightness or brightness. They are
also further used for colour matching and are valuable resources for anyone working in any
medium, e.g. printing, video, image processing, etc.
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When considering the variety of available colour systems, it is necessary to classify them into a
few categories according to their definitions. Figure 1.2 shows the most typical colour systems
that have been grouped into families of systems. These include hardware-oriented systems,user oriented systems, artificial primary systems, perceptually uniform systems and polar
coordinate systems. The shaded boxes in Figure 1.2 represent the colour spaces considered
during this research. Other families of systems include normalised systems and independent
non-correlated systems. It should be noted that all the colour systems depicted in Figure 1.2
originate from RGB, which is by far the most used colour space for the acquisition of and
display of images by using colour cameras and CRT monitors respectively.
Figure 1.2: Colour Systems and colour spaces. The highlighted colour spaces are
considered for the work presented in this thesis.
CIEXYZ
KodakYCC
IHS
HLS
HSV
TEKHVC
RGB
CMY
CMYK
Primary based
YIQ
YUV
YCrCb
NTSC
PAL (EBU)
Digital
xyz
rgb
TV based
Printing based
Hardware OrientedSystems
Normalised
Systems
Artificial Primary
Perceptual User
Oriented Systems
CIExyY
CIEL*a*b*
CIEu'v'Y
CIEL*u*v*
Perceptually Uniform
Systems
Y,S,H L,C*,Ho
Polar Coordinate
Systems
OHTA KLT
Independent Non-Correlated
Systems
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1.4.1 Hardware oriented systems
These colour systems are device dependant models [Bas92] which means that the colour
depends on the equipment and the set-up used to produce it. As an example by using a
computer CRT monitor to display colours, the colour produced using pixel values of RGB =
0,255,255 (i.e. cyan) will alter as the brightness and contrast is changed on the monitor. In the
same way, if the monitor was to be replaced, the red, green and blue phosphorus used for the
screen will have slightly different characteristics and the colour produced will change.
1.4.1.1 The RGB colour space
In this model, colours are formed by the combination of the three primary colours red, green
and blue, which makes RGB and additive system. It forms the most basic and well-known
colour model and can be seen in Figure 1.3. The different levels for each of the primary colours
can produce a wide range of colours, i.e. gamut[Gon93]. The benefit of this colour system is
the ease of implementation and therefore is widely used for colour cameras and computer
cathode ray terminals (CRT) displays. Among the disadvantages of this colour space we find its
device dependency, the high correlation of colours and the difficulty of perception by a user.
Common practice is to assume that colour similarity is inversely proportional to a distance
metric in that space. This assumption proves inappropriate for RGB, since equal distances in
this colour space rarely match perceived equivalence in similarity [Sea99].
Figure 1.3: Two views of the RGB colour space
Colour image processing algorithms based on RGB can be found in pixel clustering based on
empty or full bins of colour histograms and are reported in [Nov92]. Potential applications of
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RGB to machine vision are discussed from a perspective of physics-based vision and the
calibration procedure in [Mar96]. Zugaj and Lattuati [Zug98] employed a gradient operator,
applied on the multi-image RGB in order to produce edges in colour segmentation. A series ofsurfaces closely modelling how the human eye cone receptors respond to RGB changes give the
red/green and yellow/blue chromatic direction of the surfaces, while rod receptors being
sensitive to lightness variations give the separation of the surfaces. This produces the RGYB
colour geometry and is reported in [War90]. One-chip CCD cameras use interpolation methods
to produce full-colour images in RGB. The design of practical, high quality filter array
interpolation algorithms on a simple image model are discussed in [Ada97] and [Ada98].
The normalised components, which are called chromaticity coordinates, only take into account
chrominance. The chrominance coordinates of the RGB system are denoted rgb, where
( )BGRRr ++= , ( )BGRGg ++= and grb =1 . Colour can alternatively be
represented in the chromaticity diagram by the coordinates ( gr, ) [Wys00] [Van00].
Values of rgb coordinates are much more stable with changes in illumination than RGB
coordinates [Ber87]. For this, rgb has been used in applications such as the stability of our
perception of surface colours despite changes in illumination, i.e. colour constancy [Fin95].Early applications ofrgb can be found in analysis of aerial pictures [Ali79] and edge detection
[Nev77]. Based on the intersection of histograms, Swain and Ballard [Swa91] using an
opponent colour axis derived from rgb created an colour indexing system for image retrieval
based purely on colour properties and not on geometrical properties.
1.4.1.2 The CMY colour space
CMY or Cyan-Magenta-Yellow colour space is a subtractive model and is used primarily in
printing and photography. Printers often include a fourth component, black, to have CMYK.
Black, symbolised by K, is substituted for equal parts of CMY to lower the costs of ink and to
generate a pure black. Subtractive colours are seen when pigments in a object absorb certain
wavelengths of white, while reflecting the rest. The wavelengths that are reflected as opposed
to being absorbed are the ones perceived as colour.
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An application has been found by Verikas et al. [Ver97] to determine colours of inks used to
produce multi-coloured pictures created by printing dots of cyan, magenta, yellow and black
primary colours upon each other through screens having different raster angles. Colourreproduction process many time relies in three (CMY) or four (CMYK) colours and Herzog
[Her96] proposed a new analytical method to represent a surface of a colour gamut using a
closed expression directly in CIEL*a
*b
*based on the similarity of the gamut for the CMY cube.
1.4.1.3 The YIQ colour space
The National Television Standards Committee (NTSC) of the United States uses a colour
specification consisting of luminance (Y) and two difference colour signals called inphase and
quadrature (I and Q) [Hut90]. The YIQ exploits a useful property of our vision system. The
system is more sensitive to changes in luminance than changes in hue and saturation (i.e.
colour); that is, our ability to discriminate spatially colour information is weaker than our
ability to discriminate spatially monochromatic information [Fol90]. This result is an I-axis
encoding chrominance information along a blue-green to orange vector, and a Q-axis encoding
chrominance information along a yellow-green to magenta vector.
Applications of the YIQ space have proven to be useful in colour image coding [Ove95]
[Van94] to obtain image compression. An image retrieval system named PicSOM [Laa99] uses
the Y component to perform image querying on the World Wide Web (WWW). Speech
recognition based on video represented in YIQ for lip tracking combined with acoustic signals
can be found in [Hen95]. Boo and Bose [Boo] proposed a procedure to restore a single colour
image, which has been degraded by shift-invariant blur in the presence of additive stationary
noise. Reduction of up to 50% in the size of the frame buffer used with colour palettes to
display images on CRT computer screens is achieved by using vector quantization in the YIQ
colour space [Wu96].
1.4.1.4 The YUV colour space
This is a specification for the European Broadcast Union (EBU) and PAL and SECAM
television systems in Europe and in many other countries use the system [Car69]. It consists of
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a luminance Ysignal and two colour difference signals Uand Vthat are used as transmission
coordinates. The YUV coordinate system was initially proposed as the NTSC transmission
standard, but was replaced by the YIQ system because it was found that the I and Q signalscould be reduced in bandwidth to a greater degree than U and V for an equal level of visual
quality [Pra91].
The effect of colour quantization schemes on the performance of image retrieval with colour
clustering using YUV, RGB, HSB and CIEL*u*v* can be seen in [Wan98]. A novel technique
for efficient coding of texture to be mapped on 3-D surfaces using digital maps represented in
YUV format is given in [Hor97]. A real-time algorithm for extracting shape parameters from
facial features such as eyes and mouth has been carried out in [Rao96]. YUV colour space has
proved to be useful in the Motion Picture Experts Group (MPEG) encoding [Tor97] [Kru95].
Robotic vision using YUV colour model also is reported in the literature [Sch97] [Nak98].
1.4.1.5 The YCrCb colour space
This is a space that is independent of the TV signal coding systems and is primarily oriented to
digital television [Rob97]. The component Y for luminance is identical to that for YUV and
YIQ. The chromatic information is found in the Cr(colour red) and Cb (colour blue) signals.
Current applications in image compression (e.g. JPEG format) often employ YCrCb model as a
quantization space [Coo93] [Ler95] or also used to encode the overall prefix codes which result
from many forms of image compression algorithms such that they are largely independent of
each other [Whi98]. The chrominance sub-sampling information and the possible degrading of
colour in images is enhanced by a methodology proposed by Schmitz [Sch97a]. Localisation of
facial regions in videophone images using both the luminance and chrominace is another
application of YCrCb researched in [Cha96].
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1.4.2 User oriented systems
Other names given to the user oriented systems include: perceptualcoloursystems,perceptual
orientedsystem or computergraphics colourspace. These systems decouple the lightness,
brightness or value information from the chromatic information.
The best known and most influential of all models based on perceptual principles was proposed
in 1905 by Albert Munsell and is known as the Munsellcoloursystem which is still widely in
use to date [Mun76]. Munsell modelled his system as an orb around whose equator runs a band
of colours as can been observed in Figure 1.4. The axis of the orb is a scale of neutral grey
values with 10 divisions with white at the top and black at the bottom. Extending out, from the
axis at each grey value is a graduation of colour progressing from neutral grey to full saturation.
Munsell introduced three aspects that would describe any thousand of colours; these are
described in Munsell terms as:
Hue: The quality by which we distinguish one colour from another. Munsell selected
five primary colours: red, yellow, green, blue and purple; five intermediate colours:
yellow-red, green-yellow, blue-green, purple-blue and red-purple and made a wheel
defining 100 compass points.
Value: The quality by which we distinguish a light colour from a dark one. Value is a
neutral axis that refers to the grey contents of the colour. It ranges for black to white.
Chroma: The quality that distinguishes the difference from a pure hue to a grey scale.
The chroma axis extends from the value axis at a right angle.
A method of expanding colour images in terms of Munsells three aspects or attributes of
colour perception can be found in [Tom87].
1.4.2.1 The IHS colour space
Known sometimes as the Hue-Saturation-Intensity (HSI, IHS) model and is an intuitive model
for specifying colours. Hue refers to the name given to as colour (e.g. red, yellow, etc.) and is
represented as degrees on a colour wheel with values from o0 (red) to o120 (green) to o240
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(blue) to o360 (red again). Saturation is the purity of the colour. Low saturation (< 20%) results
in grey, regardless of the hue; middle saturation (40% to 60%) produces pastels; and high
saturation (> 80%) results on vivid colours. Intensity is the brightness of a colour and ranges
from 0% (black) to 100% (white). Sometime intensity is also referred to as luminance or
lightness [Gon93].
Figure 1.4: Munsell's colour space.
Several image processing functions can be realised using the IHS colour space, these include
segmenting colour using the three colour attributes: hue, saturation and intensity for a robust
specialised architecture for real-time tracking [Gar96]. Remote Sensing is another popular area
where IHS colour model is appropriate for monitoring various natural resources and
environmental hazards. Geohazard assessment using synthetic aperture radar1 (SAR) and
thematic mapped (RM) images are used to characterise areas affected by landslides and costal
hazards in the lower Fraser Valley within the Canadian Rockies [Sin95]. Other applications
include the study of soil salinity and alkalinity dynamics [Dwi98], image fusioning techniques
to exploit the complementary nature of multi-sensor image data [Sch98] [Sun98],
discriminating areas of hydro thermally altered material in vegetated terrain in central Brazil
1 Many people associate the word aperture with photography, where the term represents the diameter of the lens'
opening. The camera's aperture then determines the area through which light is collected. Similarly, a radar antenna's
length partially specifies the area through which it collects radar signals. The antenna's length is therefore also called
its aperture. In general the larger the antenna, the more unique information you can obtain about a particular viewed
object. With more information, you can create a better image of that object (improved resolution). It's prohibitively
expensive to place very large radar antennas in space, however, so researchers found another way to obtain fine
resolution: they use the spacecraft's motion and advanced signal processing techniques to simulate a larger antenna.
A SARantenna transmits radar pulses very rapidly. In fact, the SAR is generally able to transmit several hundred
pulses while its parent spacecraft passes over a particular object. Many backscattered radar responses are therefore
obtained for that object. After intensive signal processing, all of those responses can be manipulated such that the
resulting image looks like the data were obtained from a big, stationary antenna. The synthetic aperture in this case,therefore, is the distance travelled by the spacecraft while the radar antenna collected information about the object.
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[Alm97] and Mediterranean vegetated coastal area classification [Gri97]. Other areas of study
include the analysis of skin lesions [Fis96].
1.4.2.2 The HSV colour space
The HSV model, created by Smith [Smi78] (also called the HSB model, withB for brightness)
is an user-oriented colour model, based on the intuitive appeal of the artists tint, shade and tone
[Fol90]. The coordinate system is cylindrical and the subset of the space within the model is
defined as a hexcone, as it resembles a six-sided pyramid. Value, orV, ranges from black (0) to
white (1). Hue, orH, is measured by the angle around the vertical axis, with red ato
0 , green at
o120 , blue at o240 , and back to red at o360 . Complementary colours are displaced by o180
and these are opposite one another. The saturation, orS, is the purity of the colour. Therefore,
saturation of 100% in the model represents a pure colour and a saturation of 0% represent a
grey level. Figure 1.5 shows a 3 dimensional model of the HSV colour space.
Figure 1.5: HSV colour space. Image on left represent a full view of the colour space,
whilst view on right is a cross-section.
Some important advantages of the HSV colour space are: good compatibility with human
intuition and decoupling of the chromatic values for the achromatic values. Segmentation and
tracking of faces or facial regions in colour images where skin like regions are determined
based on colour attributes, hue and saturation, is a common application of HSV [Sob96]
[Her99] [Yan00]. Another common area for this colour space is in the research of multimedia
information, e.g. images and video [Meh97] [Xio95] where the images are usually retrieved via
some query method [And99]. Applications can also be found for the identification of biological
objects at microscopic level for cell structures [Pav96] or for automatically detecting danger
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labels on the back of containers [Jr96]. Based on the extraction of gradient discontinuities,
edge detection can be achieved using HSV [Tsa96]. Robotic vision [Pri00] and autonomous
vision-based avoidance systems [Lor97] have also been reported using this model.
1.4.2.3 The HLS colour space
Another model based on intuitive colour parameters is the HLS system used by Tektronix
[Tek90] for some of its terminals. This model has a double-cone representation that can be
thought as a deformation of HSV, in which white is pulled upward to form the upper hexcone
from the 1=V plane. The three parameters in this model are called hue (H), lightness (L) and
saturation (S) and can be seen in Figure 1.6. Hue has the same meaning as in the HSV and IHS
models. The vertical axis is this model is called lightness. At 0=L we have black, and white is
at 100=L %. Grey scale is along the L axis and the pure hues lie on the 50=L % plane.
Saturation gives the relative purity of colour, hence when =L 50% and =S 100% a pure colour
is generated.
Tektronix later created the TEKHVC (Hue, Value and Chroma) [Bas92] colour system, that
resembles the HLS only in shape but was created to have a perceptually uniform colour space
in which measured and perceived distances between colours are approximately equal [Fol90].
Some applications using HLS can be found using cluster detection combined with probabilistic
relaxation, where clusters are extracted for specific colour from HLS to locate tumours from a
bladder image [Che98]. In order to visualise trajectories in higher dimensional dynamical
systems, the HLS model has been employed in [Weg97].
1.4.3 Perceptually uniform systems
The colour models, developed by the Commission Internationale de lEclairage (CIE) [CIE86]
have the following objectives:
1. To be completely independent of any device or other means of emission or
reproduction.
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2. Perceptual colour differences recognised as equal by the human eye would correspond
to equal Euclidean distances.
Figure 1.6: The HLS colour space
As perceptually uniform colour spaces of this kind, two spaces recommended by the CIE in
1976 are mainly in use today. These are the CIEL*a
*b
*space (used for reflected light) and the
CIEL*u
*v
*space (mainly used for emitted light) [Wys00].
The CIE colour model was developed to match as closely as possible on how humans perceive
colour. The key elements of the CIE model are the definitions of the standard sources and the
specifications for a standard observer [Fai98]. The following CIE standard sources were
defined in 1931:
Source A: A tungsten-filament lamp with a colour temperature of 2845K.
Source B: A model of noon sunlight with a temperature of 4800K.
Source C: A model of average daylight with a temperature of 6500K.
Source D: Illuminant called daylight D series. Illuminant D65 (D65) with a temperature
of 6500K is the most common referenced.
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1.4.3.1 The artificial primary CIEXYZ colour space.
As mentioned in Section 1.3, the CIE standard considered the tristimulus values for red, green
and blue to be undesirable for creating a standardised colour model because of their inclusion
of negative values (Figure 1.1, left side) which were difficult to mathematically manipulate in
1931. Instead, they use a mathematical formula to convert the RGB data to a system that uses
only positive integers in order to simplify operations. The reformulated tristimulus values were
given identifiers XYZ and are shown in Figure 1.7. These values do not directly correspond to
red, green and blue but are a close approximation. The curve for the Yvalue is equal to the
curve that indicates the human eyes response to the total power of the light source. For this, Y
is called the luminance factor and the XYZ values have been normalised so that Yalways has a
value of 100 [Wys00].
Figure 1.7: The CIEXYZ artificial primary.
Obtaining the XYZ tristimulus values is only part of defining the colour and the colour itself is
easier understood in the term of hue and chroma. To make this possible, CIE used XYZ
tristimulus values to formulate a set of normalised chromaticity coordinates that are denotedxyz
(lowercase XYZ).
The chromaticity coordinates are used in conjunction with a chromaticity diagram with the
most familiar one being CIEs 1931 xyY, or CIExyY [CIE86], chromaticity diagram. Thex and
y serve as a locator for any value of hue and chroma. The third dimension, Y, is the lightness or
luminance of the colour. It extends to white from a spot perpendicular in thex andy plane. As
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the Yvalue increases and the colour becomes lighter, the range of colours, orgamut, decreases
so that the colour space at 100=Y is just a small section of the original area.
Many attempts have been made to have chromaticity diagrams which are perceptually more
uniform. Although there has not been a successful attempt to convert a nominal scale into an
interval sacke, it is worth mentioning one of the results obtained. This is actually the
chromaticity diagram currently recommended by the CIE for general use: the CIE 1976
Uniform Chromaticity Scales (UCS) [CIE86] [Fai98]. CIEuv
Y represents this system, where u
and vare the coordinates of the chromaticity diagram and Ythe luminance.
Applications using CIExyY alone are not commonly found in literature, but an automatic
system for the detection of human faces combining skin-colour image segmentation with shape
analysis showing cumulative distributions and comparing them to HSV is given in [Ter98].
1.4.3.2 The CIEL*a
*b
*colour space
The CIEL*a
*b
*system was adopted by CIE in 1976 as a model that better showed uniform
colour spacing in their values [CIE86]. It is an opponent colour system based on the earlier
(1942) system of Richard Hunter [Hun91] [Rob90] calledL, a, b. It consists of a (a*, b
*) colour
plane that maps the hue of a colour on two dimensions: a (horizontal) dimension, separating red
colours on the right (a+) from green colour on the left (a-), and the b (vertical) dimension,
separating yellow colours at the top (b+) from blue at the bottom (b-). The position of a colour
in the space represents the overall contribution of red or green, and blue or yellow to its hue.
Orange is a combination of red (a+) and yellow (b+), so orange appears in the first quadrant
(a+, b+) of the space. This is clearly depicted in Figure 1.8.
Many colour image processing algorithms, such as coding, segmentation, gamut mapping, etc.
choose CIEL*a
*b
*colour space for its desirable properties [Woe96] [Dub97]. Using hue-
correcting look-up tables, Braun and Fairchild [Bra98] made a series of experiments to test the
utility of using constant hue visual data to linearize the CIEL*a*b* space with respect to hue and
present their results.
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Figure 1.8: The CIEL*a
*b
*colour space. Figure on left shows the main axis and figure on
right a 3-D model of the colour space.
Perhaps, CIEL*a
*b
*is one of the more popular colour spaces for image processing and it has
been used in a number of applications and a few examples are listed next.
Colour removal of dyes that cause pollution originating from textile mill has been studied in
[Yeh95]. Vallart et al. [Val94] used CIEL*a*b* for the measurement of colour for the study of
painted works of art. A model and mathematical formulation for describing the light scattering
and ink spreading phenomena in printing can be found in [Emm00]. Using the L* and b*
(yellowness) parameters, a method was proposed in [Bha97] to control the temperature of
barrel and screw speed for the extrusion of a rice blend. Techniques for examining the effects
of heating soymilk were described in [Kwo99]. A method for embedding invisible information
in colour images (i.e. watermarking) was proposed in [Fle97].
Extensive experimentation has been done on CIEL*a*b* areas such as gamut mapping
techniques [Mon97], obtaining figures of merit to evaluate how close CIEL*a
*b
*matches the
actual perceived accuracy for cameras and scanners [Sha97a] [Har98] [Har99] [Har00].
In order to measure colour reproduction errors of digital images, Zhang and Waddell [Zha96]
proposed and extension to the CIEL*a
*b
*colour metric called s-CIEL
*a
*b
*and applications of
this extension can found in [Zha97].
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1.4.3.3 The CIEL*u
*v
*colour space
CIEL*
u*
v*
originates from a series of chromaticity diagrams that where inadequate because the
two-dimensional diagram failed to give uniformly-space visual representation of what is
actually a three-dimensional colour space [Wys00]. It originated first from CIExyY in 1931,
next a new set of values ),( vu that presented a visually more accurate two-dimensional model
was proposed by CIE, giving CIEuvY. However, this was still found unsatisfactory and in 1975,
CIE proposed modifying the ),( vu diagram yielding the new )','( vu values creating CIEuv
Y
that has much better visual uniformity. The final successor, in an attempt to further improve the
)','( vu diagram, gave the CIEL*u
*v
*, whereL replaces Y. This colour space is also based on the
opponent-colour theory, which models the human colour vision. In this colour space u*
represents the red-green coordinate, whilst v*
axis represents yellow-blue. TheL*
axis describes
variations in lightness. Shown in Figure 1.9 is a 3-D image of the CIEL*u*v* colour space.
Figure 1.9: The CIEL*u
*v
*colour space.
Many application can be found based on the CIEL*u
*v
*colour space. It has proved very useful
in colour image segmentation methods that use a clustering approach [Sch93] [Uch94] [Hea96].
Recognition and localization of two-dimensional objects on a known background is another
application [Kaa97]. Using lightness and saturation, Liu and Yan [Liu94a] enhanced edges on
colour images. Histogram manipulation is also popular in CIEL*u*v* to enhance images [Mls96]
and to retrieve images from video databases [Par99].
A study to examine the relationship between colorfulness judgments of images of natural
scenes and statistical parameters of chroma distribution over the images can be found in
[Yen97]. An identification experiment of naming 35 colours with no previous training was
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done by [Der95] and an association of CIE colorimetry and colour displays is given by Schanda
[Sch96].
1.4.4 Polar coordinate systems
Polar coordinate systems are basically a rectangular to polar coordinate transformation from a
particular colour space. For TV colour spaces (e.g. YIQ, YUV and YCrCb) a vector having a
magnitude S(Saturation) and an angleH(Hue) is obtained from the chroma channel expressed
in rectangular form, whilst the luminance Yremains unaltered. For perceptually uniform colour
spaces (e.g. CIEL*u*v* and CIEL*a*b*) a vector is obtained with its magnitude given by C*
(Chroma) and an angle oH (Hue). Refer to Figure 1.2.
1.4.5 Independent non-correlated systems
These systems are also known as the statistically independent component systems. They can be
determined by different methods in order to obtain non-correlated components. As shown in
Figure 1.2, Ohta [Oht80] defines a colour system using I1,I2 andI3 using the Karhunen-Loeve
Transformation (KLT) [Loe55]. This transform also is commonly referred to as the
eigenvector,principalcomponent, orHotellingtransform and is based on statistical properties
of vector representations. These systems are not included or studied in this thesis, but are
suggested in Chapter 7 as future research.
1.5 Motivation and objectives behind this work
After reviewing the literature for colour image processing algorithms and the most frequently
colour spaces used for their implementations it was decided to think of a common methodology
that could realize transformations from RGB to most of them. RGB is by far the most widely
used colour space for image acquisition (e.g. still cameras, video cameras, etc.) and displaying
images (e.g. CRT monitors). With colour image processing becoming increasingly more
popular and with the drop in cost of imaging devices and hardware it is now possible to face
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many challenges in optimizing design into a relatively low cost technology. For this, it was
decided not only to devise a universal transformation procedure, but to also implement it in
hardware with the objective of having a low cost and high-performance system. The objectivesof this research are summarized next:
1. Devise a common straightforward methodology capable of converting RGB to the most
commonly used colour spaces reported in literature taking into account that images are
obtained from charged coupled devices (CCD) still-cameras or video-cameras that
generate outputs in the RGB colour space. Also, images will frequently be displayed in
devices based on RGB (e.g. CRT computer monitors).
2. Analyze and manipulate the conversion methodology considering always that it should
be implemented in hardware.
3. When implemented in hardware, real-time conversions rates should be expected,
matching or superseding the frame-rates given by video cameras with full resolution
and colour depth.
4. Design an architecture flexible enough to permit expansion and interconnectability with
other hardware elements in order to perform some colour image processing algorithms
as well.
The colour spaces that will be considered during this work are summarized in Table 1.1
Colour System Sub-system Colour space considered
Television based systems YIQ, YUV, YCrCbHardware oriented
system Printing systems CMY
Perceptual user oriented
systemIHS, HLS, HVS
Artificial primary system CIEXYZPerceptually uniform
system CIE systems CIExyY, CIEL*a*b*,
CIEuv
Y, CIEL
*u
*v
*
Table 1.1: Colour spaces considered during this research
Some hardware applications for real-time colour image processing and colour space
transformations can be found in the literature. A real-time tracking system based on Field
Programmable Gate Arrays (FPGAs) based on colour segmentation and using the IHS colour
space was reported in [Gar96]. FPGAs where also used by Mribout et al. [Mer93] to
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implement edge detection and edge tracking, while Adraka [And96] devised a dynamic
hardware video processing platform. Reconfigurable logic [Woo98] using the XC6200 from
Xilinx
is another methodology of implementing real-time processing. Very Large ScaleIntegration (VLSI) implementations for Application Specific Integrated Circuits (ASICs) and
manipulation of digital colour images in the IHS colour space can be found in [And95] and
using the CIEL*a
*b
*in [And95a].
Integrated circuit manufacturers have been also producing dedicated circuitry for colour image
processing applications. Altera
has a RGB to YCrCb and YCrCb to RGB colour space converter
operating in real-time [Alt97]. Edge detection [Atm00] and 33x convolution [Atm00a] are also
possible using Atmel AT6000 FPGAs. Crystal semiconductors also created a digital colour-
space processor [Cry97] operating in the YCrCb colour space for CCD cameras.
1.6 Organisation of this thesis
Chapter 2 first looks at the equations needed to make transformations from RGB to alternative
colour spaces. Most of the image acquisition devices are based on the RGB colour space;
therefore this chapter reviews the algorithms, equations, procedures reported in literature to
obtain conversions. Next and after analyzing these algorithms a generalization procedure will
be proposed which is suitable to cope with all variants of the transformation procedures. This
will be done having in mind that it should also be suitable for hardware implementation. Three
stages will be identified that will cope with every possible transformation scenario and are
explained. Moreover, the last section of the chapter determines all the ranges of possible values
used by the generalised conversion procedure. This is of vital importance for hardware
implementation to consider the resolution and size of all units that will carry out the arithmetic
operations and to determine the word and bus sizes.
Chapter 3 will explain in detail how the generalized transformation methodology described in
Chapter 2 is realized in hardware. A Universal Colour Transformation Hardware (UCTH)
based on Field Programmable Gate Arrays (FPGAs) and Look-up Tables (LUTs) capable of
performing real-time colour transformations is outlined and implemented here. LUTs will
contain the final values of the resulting transformed colour space, whilst the FPGAs will be
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reducing the size and generating the address buses for each and every look-up table. To further
simplify equations, a cascaded LUT configuration is also proposed and implemented. This
chapter will also be covering how floating-point numbers will be represented using fixed-pointvalues on which mathematical operations can be performed minimizing quantization errors. The
next sections of this chapter will be devoted to implement each of the three stages mentioned in
Chapter 2, which are: a Matrix Multiplier (MM), a Functional Mapping Uunit (FMU) and
Switching Interconnect Logic (SIL). At the end of Chapter 3, the devices used for the
implementation will be described.
Chapter 4 will be devoted to fully test the operation and functionality of the UCTH. Two tests
will be performed: namely a static test with fixed images and a dynamic test with video images.
During the static tests, the results generated by modeling the transformations in software,
simulating the circuitry using the design entry tools and hardware simulations will be verified
and conclusions will be drawn. A formal verification procedure is established to demonstrate
the functionality and precision of the architecture as a whole. With the dynamic testing of the
circuitry, conversion speeds will be obtained to prove the UCTH capability to handle real-time
transformation speeds. A series of support circuitry and units are designed to interface the
UCTH with input and output devices. These will include, a Colour Conditioning Unit (CCU), a
Digitizing Unit (DU), a Filtering Unit (FU) for noise removal and finally generating video
signals that can be displayed on a CRT monitor. Furthermore, this chapter will be incorporating
a section to the UCTH named the colour image processing unit (CIPU) that will be able to
carryout some image processing algorithms to demonstrate the flexibility of the UCTH.
Chapter 5 will be implementing two different colour image-processing algorithms using the
CIPU. First, an image segmentation algorithm based on colour clustering will be realised. It
will be based on the CIEL*
a*
b*
colour space. Regions of pixels will be grouped together based
on their colour properties and delimited by planes creating a cluster volume. Second, a colour
defective vision graphics display will be put into operation also showing the versatility of the
UCTH and the CIPU module. Basically it permits people with no colour deficiencies,
whatsoever, to see through the eyes of people with some kind of colour blindness. The
objectives and uses of these realisations will also be explained.
Chapter 6 deals with the design and implementation in hardware of a 2-D median filter for the
removal of impulse noise from images. By using a novel rank adjustment technique that
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prevents implicit sorting and moving values around, a highly repetitive structure is obtained
which is suitable for hardware completion. A clear procedure of how the technique works is
given by means of an illustrative example. The filter is capable of operating in real-time givinga median output value every clock cycle regardless the size of the input mask. The front-end of
the filter will be converting a linear stream of pixels originating from a CCD camera to a matrix
form with the aid of First-In First-Out (FIFO) memories. At the back-end of the filter, a field
memory will be holding the filtered channel. This chapter also reviews more appropriate used
filters for higher dimension spaces, i.e. vector directional filters (VDF) and vector median
filters (VMF).
Chapter 7 contains the conclusions and suggestions for future work. Comments and
conclusions will be given for the results obtained in every chapter. Finally, suggestions for the
line of research that can be followed in this work will be highlighted.
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